Improve Text Classification Accuracy by 10% to 15%

Concluído Postado Dec 29, 2013 Pago na entrega
Concluído Pago na entrega

I have an SQL database schema with thousands of events (30,000 to be exact but only about 5,500 are currently usable). What I'd like to do is improve my current 70% f1-score by at least 10% (I can get nearly 90% success with the 30,000, but not with the 5,500). I am currently using Scikit-Learn's machine learning library. It's written in python.

What I'd prefer is someone who could simply apply better feature selection. I've implemented the Chi-squared and best-features options within Scikit, but I've had limited success. I'd like someone who could explain how to implement a high-value terms selection criterion. Something along these lines: [url removed, login to view]

You don't need to write the code yourself -- tho if you're interested I'll provide the database schema, so you can apply a library of your choice (preferably Scikit or Solr). I need someone who can help explain the process. I can write everything. I'm just not an ML expert. Thank you in advance for any interest.

Apache Solr Machine Learning (ML) Python Arquitetura de software

ID do Projeto: #5269629

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5 propostas Projeto remoto Ativo em Mar 15, 2014

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ghazalpasha

My suggestion (assuming you have a very large dictionary/feature space): - Do TFIDF (if you haven't done done already) - Do hashing on features to reduce the size of your feature space (this works surprisingly well i Mais

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roboboysl

I have previous experience in ,achine learning development, I would like to know more about the project....

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raga2020

Hey, I have done a project on Machine Learning : Multi Agent Based Sentiment Anaysis where we used NLTK to analyse the Parts of Speech og the text and classify it using Beayes Theorem by assigning probability of occ Mais

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eecs93

Hello, I have a Masters of Science in Electrical Engineering and over a decade of Matlab experience. Please provide more information about this project and I can adjust my bid ($ & Time). Thank you for your cons Mais

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